A Data Augmentation Framework to Improve R-peak Detection in ECG Recorded in MRI Scanners

Detecting R-peaks in ECG using deep learning (DL) requires large, annotated datasets. Such datasets with the MRI-specific magneto-hydrodynamic (MHD) effect, do not exist currently. We propose a robust data augmentation framework using records available online, adding realistic MHD artifacts to augment the training dataset. MHD artifacts were modelled from eight 3T MRI-ECG records, and added to 75 non-MRI-ECG records. The R-peak detection was evaluated on six 3T MRI-ECG records. Compared to a DL model trained without data augmentation, the number of false positives and missed detections were reduced by 57.6% and 16.4%, the overall error was decreased by 25%.